// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "dtype.h"
#include "matmul_helper.h"
#include "my_types.h"
#include "paddle/extension.h"
#include "paddle/phi/core/kernel_registry.h"
template <typename T>
void AvxCompute(const paddle::Tensor &x,
                const paddle::Tensor &weight,
                const paddle::Tensor &w_bias,
                bool trans,
                const std::string alog,
                paddle::Tensor &out,
                xft::Matrix<T> &quantizedWeight,
                xft::Vector<float> &WeightScale,
                xft::Vector<float> &WeightZero,
                xft::Vector<float> &WeightSum,
                MMHelper *mmHelper) {
    auto out_data = out.data<float>();
    const float *x_data = reinterpret_cast<const float *>(x.data<float>());
    const float *bias_data = nullptr;
    if (w_bias.initialized()) {
        bias_data = reinterpret_cast<const float *>(w_bias.data<float>());
    }
    int m = 1;
    for (int i = 0; i < x.shape().size() - 1; i++) {
        m = m * x.shape()[i];
    }
    int k = x.shape()[x.shape().size() - 1];
    int l = weight.shape()[1];
    int n = weight.shape()[1];
    if (w_bias.initialized()) {
        mmHelper->compute_bias(false,
                               m,
                               n,
                               k,
                               1.0f,
                               x_data,
                               k,
                               quantizedWeight.Data(),
                               WeightScale.Data(),
                               WeightZero.Data(),
                               WeightSum.Data(),
                               0.0f,
                               out_data,
                               l,
                               bias_data);
    } else {
        mmHelper->compute(false,
                          m,
                          n,
                          k,
                          1.0f,
                          x_data,
                          k,
                          quantizedWeight.Data(),
                          WeightScale.Data(),
                          WeightZero.Data(),
                          WeightSum.Data(),
                          0.0,
                          out_data,
                          l);
    }
};
template <typename T>
void AvxWeightOnly(const paddle::Tensor &x,
                   const paddle::Tensor &weight,
                   const paddle::Tensor &w_bias,
                   bool trans,
                   const std::string alog,
                   paddle::Tensor &out) {
    static std::unordered_map<std::string,
                              std::tuple<xft::Matrix<T> *,
                                         xft::Vector<float> *,
                                         xft::Vector<float> *,
                                         xft::Vector<float> *>>
        weight_only_hub;
    std::stringstream weights_addr;
    weights_addr << weight.data<float>() << alog;
    std::string weight_only_key = weights_addr.str();
    auto it_created = weight_only_hub.find(weight_only_key);
    static MMHelper *mmHelper;
    int rows = weight.shape()[0], cols = weight.shape()[1];
    xft::Vector<float> *WeightScale =
        new xft::Vector<float>();  // if weight is int8
    xft::Vector<float> *WeightZero =
        new xft::Vector<float>();  // if weight is int8
    xft::Vector<float> *WeightSum =
        new xft::Vector<float>();  // if weight is int8
    xft::Matrix<T> *quantizedWeight = new xft::Matrix<T>();
    if (it_created == weight_only_hub.end()) {
        auto weight_ptr = reinterpret_cast<const float *>(weight.data<float>());
        xft::Matrix<T> convertedWeight;
        mmHelper = new MMHelper(xft::DeviceKind::iCPU, 0);
        mmHelper->convertWeight(trans,
                                rows,
                                cols,
                                weight_ptr,
                                nullptr,
                                nullptr,
                                convertedWeight,
                                *WeightScale,
                                *WeightZero,
                                *WeightSum);
        quantizedWeight->Resize(rows, cols);
        mmHelper->packWeight(trans, convertedWeight, *quantizedWeight);
        weight_only_hub[weight_only_key] = std::make_tuple(
            quantizedWeight, WeightScale, WeightZero, WeightSum);
        AvxCompute<T>(x,
                      weight,
                      w_bias,
                      trans,
                      alog,
                      out,
                      *quantizedWeight,
                      *WeightScale,
                      *WeightZero,
                      *WeightSum,
                      mmHelper);
    } else {
        AvxCompute<T>(x,
                      weight,
                      w_bias,
                      trans,
                      alog,
                      out,
                      *(std::get<0>(it_created->second)),
                      *(std::get<1>(it_created->second)),
                      *(std::get<2>(it_created->second)),
                      *(std::get<3>(it_created->second)),
                      mmHelper);
    }
}
std::vector<paddle::Tensor> InvokeAvxWeightOnly(const paddle::Tensor &x,
                                                const paddle::Tensor &weight,
                                                const paddle::Tensor &w_bias,
                                                const std::string &alog,
                                                bool trans) {
    auto out_shape = x.shape();
    out_shape[out_shape.size() - 1] = weight.shape()[1];
    auto out = paddle::empty(out_shape, x.dtype(), paddle::CPUPlace());
    if (alog == "int8") {
        AvxWeightOnly<int8_t>(x, weight, w_bias, trans, alog, out);
    } else if (alog == "fp16") {
        AvxWeightOnly<float16_t>(x, weight, w_bias, trans, alog, out);
    } else {
        AvxWeightOnly<float16_t>(x, weight, w_bias, trans, alog, out);
    }
    return {out};
}

std::vector<std::vector<int64_t>> AvxWeightOnlyInferShape(
    std::vector<int64_t> x_shape,
    std::vector<int64_t> weigh_shape,
    std::vector<int64_t> weigh_bias_shape) {
    int m = 1;
    for (int i = 0; i < x_shape.size() - 1; i++) {
        m = m * x_shape[i];
    }
    return {std::vector<int64_t>{m, weigh_shape[1]}};
}

std::vector<paddle::DataType> AvxWeightOnlyInferDtype(
    paddle::DataType x_dtype,
    paddle::DataType weight_dtype,
    paddle::DataType weight_bias_dtype) {
    return {x_dtype};
}

PD_BUILD_STATIC_OP(avx_weight_only)
    .Inputs({"x", "weight", "w_bias"})
    .Outputs({"out"})
    .Attrs({"alog: std::string", "trans:bool"})
    .SetKernelFn(PD_KERNEL(InvokeAvxWeightOnly))
    .SetInferShapeFn(PD_INFER_SHAPE(AvxWeightOnlyInferShape))
    .SetInferDtypeFn(PD_INFER_DTYPE(AvxWeightOnlyInferDtype));
